Explore the recent global developments with R

Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.

Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Look at the data

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

unique(gapminder$year)
##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop, label="country")) +
  geom_point() + geom_text(aes(label=country), hjust=1.4, vjust="1") +
  scale_x_log10()

We see an interesting spread with an outlier to the right. Answer the following questions, please:

Q1. Why does it make sense to have a log10 scale on x axis?

The gap between the highest value and the lowest value in terms of gdpPercap is too large. Therefore, in order to meaningfully be able to visualize all values of gdpPercap, the x-axis has to increase tenfold

Q2. What country is the richest in 1952 (far right on x axis)?

On the plot we see a small dot far away from the others. I first tried to put labels on the points with geom_text(aes(label = country)), but as we can see, the text is simply to small to be able to see. I can somewhat see that it says Kuwait, but in order to be certain, i sort the dataset in terms of gdpPercap for 1952 and see that it is in fact Kuwait.

subset(gapminder[order(-gapminder$gdpPercap),], year == 1952)
## # A tibble: 142 x 6
##    country        continent  year lifeExp       pop gdpPercap
##    <fct>          <fct>     <int>   <dbl>     <int>     <dbl>
##  1 Kuwait         Asia       1952    55.6    160000   108382.
##  2 Switzerland    Europe     1952    69.6   4815000    14734.
##  3 United States  Americas   1952    68.4 157553000    13990.
##  4 Canada         Americas   1952    68.8  14785584    11367.
##  5 New Zealand    Oceania    1952    69.4   1994794    10557.
##  6 Norway         Europe     1952    72.7   3327728    10095.
##  7 Australia      Oceania    1952    69.1   8691212    10040.
##  8 United Kingdom Europe     1952    69.2  50430000     9980.
##  9 Bahrain        Asia       1952    50.9    120447     9867.
## 10 Denmark        Europe     1952    70.8   4334000     9692.
## # … with 132 more rows

You can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(color = continent)) +
  scale_x_log10()

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Q3. Can you differentiate the continents by color and fix the axis labels?

We can make ggplot color the different points automatically by some group using the “color” parameter in the geom_point function. This is done by writing geom_point(aes(color = continent)). The aes function is used for changing aesthetics. ggplot will then add the label on the axes by itself.

Q4. What are the five richest countries in the world in 2007?

In Q2 i used subset to confirm the richest country in 1952. By changing the year to 2007 and wrapping it in the head() function, the parameter 5 allows us to only show the top 5 richest country as it has already been sorted by the order-function

head(subset(gapminder[order(-gapminder$gdpPercap),], year == 2007), 5)
## # A tibble: 5 x 6
##   country       continent  year lifeExp       pop gdpPercap
##   <fct>         <fct>     <int>   <dbl>     <int>     <dbl>
## 1 Norway        Europe     2007    80.2   4627926    49357.
## 2 Kuwait        Asia       2007    77.6   2505559    47307.
## 3 Singapore     Asia       2007    80.0   4553009    47143.
## 4 United States Americas   2007    78.2 301139947    42952.
## 5 Ireland       Europe     2007    78.9   4109086    40676.

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. And there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the viz inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

options(scipen = 999)
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  ggtitle("Year: {frame_time}") +
  theme(plot.title = element_text(hjust = 0.5)) +
  transition_time(year)
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Q5 Can you add a title to one or both of the animations above that will change in sync with the animation? [hint: search labeling for transition_states() and transition_time() functions respectively]

In this case, the latter method seems to be the best, so i’ve only added to that one. The transition_time function gives us acces to a frame_time variable, that holds each year. By inserting that using ggtitle() and then adding centering in the theme() function, i can put each year of the transition as the title.

Q6 Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.[hint:search disabling scientific notation]

It seems that there are multiple ways to do this, but the simplest is using the options method. Here we can set a “penalty” for the notation, and by setting it to 999, we ensure that it will never abreviate the labels. options(scipen = 999)

Q7 Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]

While i do not have the skills to make this a very beatiful plot, i wanted to answer the question: “How has the population changed for each continent and country over the years”. Using face_wrap on continent, i create a separate plot for each continent, as this makes the difference in continents more obvious. I have labeled each point and i’m sure this could be better, but i’m not sure how. Bottomline is, that we can see in Asia how china and india just accelerates over the course of the years while many countries more steadily grows in population size. 

anim3 <- ggplot(gapminder, aes(year, pop)) +
  geom_point(aes(color = continent)) + 
  geom_text(aes(label=country), hjust=1.4, vjust="1") +
  scale_x_log10() + # convert x to log scale
  facet_wrap(~continent) +
  ggtitle("Year: {frame_time}") +
  theme(plot.title = element_text(hjust = 0.5)) +
  transition_time(year)
anim3